In the context of influence functions [1]}, [2]} and outlier analysis, the effect of deviation from the nominal \(p_i = 1/s\)  becomes important. The case \(p_i = 0\)  indicates a data point that the training process should ignore and this makes \(g(x;\xi _i,\eta _i)=0\)  admissible. However, a small change away from \(p_i = 0\)  would bar any such \(x\) .
